David W. Messinger

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This paper describes a collaborative collection campaign to spectrally image and measure a well characterized scene for hyperspectral algorithm development and validation/verification of scene simulation models (DIRSIG). The RIT Megascene, located in the northeast corner of Monroe County near Rochester, New York, has been modeled and characterized under the(More)
We continue previous work that generalizes the traditional linear mixing model from a combination of endmember vectors to a combination of multi-dimensional affine endmember subspaces. This generalization allows the model to handle the natural variation that is present is real-world hyperspectral imagery. Once the endmember subspaces have been defined, the(More)
Immediately following the 12 January 2010 earthquake in Haiti, a disaster response team from Rochester Institute of Technology, ImageCat Inc., and Kucera International, funded by the Global Facility for Disaster Reduction and Recovery group of the World Bank, collected 0.15 m airborne imagery and two points/m 2 lidar data for 650 km 2 over a period of seven(More)
The ability to detect and identify effluent gases is, and will continue to be, of great importance. This would not only aid in the regulation of pollutants but also in treaty enforcement and monitoring the production of weapons. Considering these applications, finding a way to remotely investigate a gaseous emission is highly desirable. This research(More)
Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or(More)
Detection of gaseous effluent plumes from airborne platforms provides a unique challenge to the remote sensing community. The measured signatures are a complicated combination of phenomenology including effects of the atmosphere, spectral characteristics of the background material under the plume, temperature contrast between the gas and the surface, and(More)
A new algorithm, optimized land surface temperature and emissivity retrieval (OL-STER), is presented to compensate for atmospheric effects and retrieve land surface temperature (LST) and emissivity from airborne thermal infrared hyperspectral data. The OLSTER algorithm is designed to retrieve properties of both natural and man-made materials.(More)
Spectral image analysis problems often begin by performing a preprocessing step composed of applying a transformation that generates an alternative representation of the spectral data. In this paper, a transformation based on a Markov-chain model of a random walk on a graph is introduced. More precisely, we quantify the random walk using a quantity known as(More)